What is Machine Learning?
Machine learning is a form of AI that mimics how humans learn, in order to create systems and devices that can learn and improve their results without additional human input. Machine learning uses data and algorithms to enable systems that can learn and gradually improve their operations and functions from their own experience, without manual programming by a human. Machine learning primarily focuses on the development of computer programs to access data automatically and then use that data to improve.
Today, machine learning is used in many everyday appliances, from robot vacuum cleaners to software mapping applications that estimate your commute time, to the varying prices of your preferred smartphone-based ride-hailing taxi service. Your cloud-based email service also learns over time how to keep your inbox free of spam, while an e-commerce website will automatically suggest products to you based on your shopping history.
AI vs machine learning
While AI and machine learning are closely related and occasionally overlap in discussion, they are not the same thing.
AI uses data and logic to simulate the reasoning that people would use to make decisions based on new information and experiences—essentially mimicking human cognitive function. Machine learning is regarded as a subset of AI. Its process employs data models to assist a computer in learning without direct instruction after the data has been set. This allows a computer to continue learning and improving on its own, as well as to form new habits based on what it has learned.
The three main approaches to Machine Learning
Machine learning is divided into three main approaches. This depends on the nature of the feedback available to the learning system.
Supervised: the machine is provided with example inputs the preferred outputs are given to it and then the goal is to learn a general rule that maps those inputs and outputs.
Unsupervised: unlike supervised learning, no preferred outputs are given, leaving the machine to find its own structure based on its inputs. Unsupervised learning is usually used to find hidden patterns in data or as a means towards an end – feature learning.
Reinforcement: the machine programme is connected to a dynamic environment where it has a certain goal. For example, it is connected to a vehicle’s driving or a video game against an opponent. As it continues to be used and perform actions, it provides feedback linked to rewards and success, which it will continue to achieve more efficiently.